Boston Logan International Airport Research Articles

Time Study for the Power Stow Rollertrack Conveyor for Airport Baggage Handling

A time study was conducted to evaluate the operation efficiency and the risk of musculoskeletal disorders (MSDs) for using the Power Stow Rollertrack Conveyor (PSRC) for baggage handling in the cargo holds of narrow-bodied aircrafts. The PSRC employs a retractable roller conveyor from a belt loader to provide powered transportation for loading and unloading baggage in the cargo holds. Thirteen baggage handlers at the Boston Logan International Airport participated in the data collection, which involved videotaping their work postures and methods during baggage handling operations in the cargo holds of the Boeing 737 and 757 aircrafts. Results showed that the PSRC provided improved efficiency in handling baggage, especially for unloading baggage by about 2 bags per minute. There was no significant difference in the total time spent on the risk factors for MSDs, such as lifting, pushing and pulling tasks per person for each bag between PSRC users and non-users.

Assessing the impact of aircraft arrival on ambient ultrafine particle number concentrations in near-airport communities in Boston, Massachusetts

Aircraft emissions contribute to overall ambient air pollution, including ultrafine particle (UFP) concentrations. However, accurately ascertaining aviation contributions to UFP is challenging due to high spatiotemporal variability along with intermittent aviation emissions. The objective of this study was to evaluate the impact of arrival aircraft on particle number concentration (PNC), a proxy for UFP, across six study sites 3–17 km from a major arrival aircraft flight path into Boston Logan International Airport by utilizing real-time aircraft activity and meteorological data. Ambient PNC at all monitoring sites was similar at the median but had greater variation at the 95th and 99th percentiles with more than two-fold increases in PNC observed at sites closer to the airport. PNC was elevated during the hours with high aircraft activity with sites closest to the airport exhibiting stronger signals when downwind from the airport. Regression models indicated that the number of arrival aircraft per hour was associated with measured PNC at all six sites, with a maximum contribution of 50% of total PNC at a monitor 3 km from the airport during hours with arrival activity on the flight path of interest (26% across all hours). Our findings suggest strong but intermittent contributions from arrival aircraft to ambient PNC in communities near airports.

Artificial neural network models for airport capacity prediction

This paper proposes artificial neural network models to predict the arrival/departure capacity of airports. Multilayer perceptron (MLP), recurrent neural networks (RNN), and long short-term memory (LSTM) models have been trained using capacity and meteorological data from Hartsfield–Jackson Atlanta International Airport (ATL) from 2013 to 2017. The models’ predictive performances were validated against the observed capacity of ATL in 2018. The qualitative and quantitative analysis of the trained models confirmed that the artificial neural networks approach is effective in predicting airport capacity. In addition, the transferability of the models for Boston Logan International Airport (BOS) is examined. Capacity prediction performance for BOS measures the transferability of the models trained with the ATL data. MLP showed good transferability without taking any other measures, and RNN and LSTM were able to predict the BOS capacity well after fine-tuning.

Flight Delay Classification Prediction Based on Stacking Algorithm

With the development of civil aviation, the number of flights keeps increasing and the flight delay has become a serious issue and even tends to normality. This paper aims to prove that Stacking algorithm has advantages in airport flight delay prediction, especially for the algorithm selection problem of machine learning technology. In this research, the principle of the Stacking classification algorithm is introduced, the SMOTE algorithm is selected to process imbalanced datasets, and the Boruta algorithm is utilized for feature selection. There are five supervised machine learning algorithms in the first-level learner of Stacking including KNN, Random Forest, Logistic Regression, Decision Tree, and Gaussian Naive Bayes. The second-level learner is Logistic Regression. To verify the effectiveness of the proposed method, comparative experiments are carried out based on Boston Logan International Airport flight datasets from January to December 2019. Multiple indexes are used to comprehensively evaluate the prediction results, such as Accuracy, Precision, Recall, F1 Score, ROC curve, and AUC Score. The results show that the Stacking algorithm not only could improve the prediction accuracy but also maintains great stability.

Airport noise and performance-based navigation: A force for good or evil?

The global aviation system is in the midst of modernisation. This includes a transition to satellite-based navigation, which offers opportunities for improved safety, efficiency and predictability. This paper posits that — despite some false starts — performance-based navigation (PBN) also offers opportunities to address noise problems. PBN relies on satellites for navigation, rather than ground-based navigational aids. The improved precision of PBN routes generally results in concentration of flights into narrow corridors. When implemented without consideration of community impacts, it has resulted in significant community outcry (even lawsuits), but when implemented with care, it can offer potential to avoid noise-sensitive areas. Our experience shows that the most successful projects are those in which the airport has been fully engaged and in which there is extensive and meaningful collaboration between all stakeholders: air navigation service providers (ANSP), industry (including airlines and other operators), airports and communities. Participants should not expect this to be easy — it is a lengthy and expensive process — but one that may be necessary for long-term compatibility and for airport growth. This paper provides an overview of PBN development in the USA as well as case studies of implementation of PBN at Phoenix Sky Harbor International Airport (AZ), Oakland International Airport (CA) and Boston Logan International Airport (MA). The case studies illustrate the increasing collaboration between stakeholders and show that progress can be made.

Jetblue Airways Versus American Airlines: A Competitive Dynamics Game

This exercise has been used in Darden's first-year strategy course in conjunction with the Airways versus American case series and is appropriate for any strategy course in a module addressing competitive dynamics. The purpose of this game is to explore the interdependencies of firms in competitive decision-making situations. The game requires pairs of competing firms to make a series of decisions over 12 rounds, each representing one month in a calendar year. Teams consist of a VP, a Regulator, and one or more strategic advisers. At the beginning of every month, each team has to choose one of the two pricing options to use that period. Teams will be given cards representing the two options for their respective firms. At the end of 12 rounds, the Regulators will add up the monthly profits for each team and report them to the Facilitator, who will announce industry and firm profitability. To help with calculating profits, a spreadsheet (UVA-S-0250X) is available. Excerpt UVA-S-0250 Rev. Sept. 15, 2015 JetBlue Airways versus American Airlines: A Competitive Dynamics Game On Wednesday, January 7, 2004, JetBlue commenced service from Boston's Logan International Airport. The same day, American Airlines announced a new promotion, “Fly Two to the Sun,” offering its frequent fliers a free round-trip ticket to anywhere in the world if they took two nonstop flights from Logan. In response, JetBlue was considering offering $ 69 one-way tickets on most flights originating from Boston—a substantial 22% off normal fares. In anticipation, American Airlines contemplated a similar pricing move to keep current customers and to potentially scare JetBlue out of the Boston market. Analysts predicted that a price war would have two immediate effects: a moderate increase in demand, given lower fares, and a decrease in operating profit per available seat mile, given the decline in revenues. But if one airline adopted lower prices while the other did not, the economics would favor the one making the price cuts. In other words, the decrease in price would offset the substantial increase in demand one would receive as the sole low-price provider of air travel from the Boston market. (See Exhibit 1 for a full analysis.) In addition, in an internal analysis, American Airlines had estimated that for every new passenger who took advantage of low fares from American, there was a 5% likelihood that the passenger would enroll in American's frequent flyer program and become a loyal customer. American Airlines had found that such loyal customers were unlikely to abandon the airline even if fares increased compared to its rivals. . . .

Demonstration of reduced airport congestion through pushback rate control

Airport surface congestion results in significant increases in taxi times, fuel burn and emissions at major airports. This paper describes the field tests of a congestion control strategy at Boston Logan International Airport. The approach determines a suggested rate to meter pushbacks from the gate, in order to prevent the airport surface from entering congested states and to reduce the time that flights spend with engines on while taxiing to the runway. The field trials demonstrated that significant benefits were achievable through such a strategy: during eight four-hour tests conducted during August and September 2010, fuel use was reduced by an estimated 12,250–14,500kg (4000–4700 US gallons), while aircraft gate pushback times were increased by an average of only 4.4min for the 247 flights that were held at the gate.

A Decision Support Tool for the Pushback Rate Control of Airport Departures

Airport surface congestion control has the potential to mitigate the increase in taxi times and fuel burn at major airports. One possible class of congestion control strategies predicts the departure throughput, and recommends a rate at which to release aircraft pushbacks from the gate. This paper describes the field-testing of these types of strategies at Boston Logan International Airport, focusing on the communication of the suggested rate to the air traffic controller, and additional support for its implementation. Two Android tablet computers were used for the field-tests; one to input the data and the other to display the recommended rate to the air traffic controllers. Two potential decision-support displays were tested: a rate control display that only presented a color-coded suggested pushback rate and a volume control display that provided additional support to the controllers on the number of aircraft that had called-ready and had been released. A survey of controllers showed that they had found the decision-support tool easy to use, especially the additional functionality that is provided by the aircraft volume control display. The field tests were also found to yield significant operational benefits showing that such a congestion control strategy could be effective in practice.

Abstract P245: Dry Air Triggers Paroxysmal Atrial Fibrillation in Patients with Implantable Cardioverter Defibrillators

Introduction: Atrial fibrillation (AF) is responsible for substantial morbidity and mortality, yet little is known about the environmental factors that may trigger AF episodes. Weather has been proposed as a potential precipitant of paroxysmal AF. Hypothesis: We assessed the hypothesis that changes in outdoor ambient temperature and outdoor absolute humidity may trigger paroxysmal AF in the following 24 hours. Methods: Patients with dual chamber implantable cardioverter defibrillators (ICDs) living in eastern Massachusetts were enrolled between September 2006 and March 2010 from the Tufts Medical Center Cardiac Arrhythmia Clinic (Boston, Massachusetts) and followed prospectively for episodes of paroxysmal AF lasting at least 30 seconds. Arrhythmias documented by the ICD were reviewed and interpreted by an electrophysiologist masked to exposure. The date and time of AF episodes were linked to hourly weather data measured at Boston Logan International Airport. We examined the hypothesis using a time-stratified case-crossover analysis matched on calendar month, day of week and time of day and adjusted for air pollution and barometric pressure. To reduce the possibility of over-representation of clustered events and to reflect the averaging time of the exposure, incident AF episodes were included in the analysis if at least 24 hours had passed since the previous AF episode. Results: Of 200 patients enrolled, 49 experienced at least one AF episode (n = 328 episodes). After excluding events with missing exposure or covariate information, analyses were confined to 230 AF episodes among 47 patients. The 47 patients with paroxysmal AF were followed for an average of 1.9 years, and 32 (68%) were male, 45 (96%) were Caucasian, the mean age was 67 years, 24 (51%) had a history of prior AF episodes, 27 (57%) had a pre-implantation ejection fraction < 35%, and 29 (62%) received the ICD for primary prophylaxis. Outdoor temperature in the previous 24 hours was not associated with AF (odds ratio (OR) = 1.13; 95% confidence interval (CI): 0.89, 1.43, p = 0.32, per interquartile range (IQR) decrease of 6.3°C). Lower absolute humidity in the previous 24 hours was associated with a higher risk of AF onset (OR = 1.33; 95% CI: 1.02, 1.74, p = 0.04, per IQR decrease of 3.5 g/m 3 ); this risk increased slightly for the previous 3 hours (OR = 1.39; 95% CI: 1.06, 1.82, p = 0.02, per IQR decrease of 3.7 g/m 3 ). Conclusions: In conclusion, drier air was associated with a transiently higher risk of paroxysmal AF in the following 24 hours among patients with dual chamber ICDs.

Network Congestion Control of Airport Surface Operations

This paper presents a novel approach to managing the aircraft taxi-out process at airports, by posing the problem in a network congestion control framework. A network model of the airport surface with stochastic link travel times is first developed. The model parameters for Boston Logan International Airport are estimated using empirical data from a surface surveillance system. Using the entry times of aircraft into the network as the control variables, a control algorithm that aims to reduce taxi-out times is proposed. This algorithm regulates network congestion while limiting the adverse effect on throughput performance. Performance characteristics of the proposed control algorithm are derived using simplified theoretical analysis, and are shown to agree with simulations of traffic on a network model of Boston Logan International Airport.

JetBlue and airline–airport partnerships

In its brief 15-year history, New York-based JetBlue Airways has evolved from mere concept to a strong, viable, low-fare alternative to America's legacy network carriers and a survivor in an industry littered with the names of defunct carriers. JetBlue's successful evolution through one of aviation's most turbulent times, moving from a niche player in New York to a leading airline across the Americans and a member of the Fortune 500, was enabled in large part by solid working relationships with airport authorities and government officials that recognised the competitive value an airline like JetBlue could deliver to communities. JetBlue's experience at three airports outlined here – Boston Logan International Airport (BOS), New York's John F. Kennedy International Airport (JFK), and Ronald Reagan Washington National Airport (DCA) – is illustrative of the key role those positive relationships can play in creating conditions for success across three stages of the carrier life-cycle: market introduction, expansion, and long-term sustainability. These are among the countless examples in which airports have recognised the tremendous benefit the carrier can bring to their facilities in the form of lower fares and increased traffic – and have seized on those opportunities to create an environment where all parties can thrive.

Weather and Triggering Ventricular Arrhythmias in Patients with Implantable Cardioverter Defibrillators

Background Several studies have examined the association between outdoor ambient weather and ventricular arrhythmia (VA) with mixed results. Because people spend most of their time indoors, we hypothesized that weather-related arrhythmia risk is linked to indoor temperature and water content, a property of outdoor air that is conserved indoors. Aims We examined the association between weather and VA using three weather measures – outdoor ambient temperature, estimated indoor temperature, and outdoor absolute humidity. Methods The clinical records of 203 patients implanted with a cardioverter defibrillator at Tufts Medical Center (Boston, Massachusetts, USA) between June 1, 1995 and December 31, 1999 were abstracted for VAs between 1995 and 2002. We used case-crossover methods to link National Weather Service measurements made at Boston Logan International Airport to the date and time of VAs (specifically ventricular tachycardia and ventricular fibrillation). Indoor temperature was estimated from the outdoor temperature using results from an indoor weather study that characterized the relationship between indoor and outdoor temperature in Boston. We matched on month, day of the week, and hour, and adjusted for barometric pressure, ozone, and particulate matter in conditional logistic regression models. Results Among 84 patients who experienced 787 VAs (mean follow-up = 3.5 years), lower estimated indoor temperature (odds ratio (OR) = 1.16, 95% confidence interval (CI): 1.06 - 1.27 for an interquartile range (IQR) (0.97°C) decrease in the 24-hour average) and lower absolute humidity (OR = 1.37, 95% CI: 1.21 - 1.56 for an IQR (2.7 g/m3) decrease in the 96-hour average) were associated with increased risk. Lower outdoor temperature increased risk only in warmer months, likely attributable to the poor correlation between outdoor and indoor temperature during cooler months. Conclusions We observed associations between lower indoor temperature and drier air with increased onset of VA among patients with implantable cardioverter defibrillators.

Metrics to Characterize Airport Operational Performance Using Surface Surveillance Data

Detailed surface surveillance datasets from sources such as the Airport Surface Detection Equipment, Model-X (ASDE-X) can potentially be used to analyze airport operations, in addition to their primary purpose of enhancing safety. This paper describes how surface surveillance data can be used to measure airport performance characteristics in three different ways: charactering surface flows, including identification of congestion hotspots, queue dynamics and departure throughput; developing metrics to evaluate the daily operational performance; and developing metrics to gauge long-term performance across different runway configurations and operating conditions. The proposed metrics have been developed with active feedback from operations personnel at Boston Logan International Airport, and are therefore evaluated and discussed using this airport as an example. These metrics can provide useful feedback on operational performance to airport operators, and, therefore, have the potential to improve the efficiency of surface operations at airports.

Airports and Bicycles

Case studies examined how airport operators addressed bicycle access to their properties and the motivations and obstacles that they faced in light of new policies to integrate bicycles, along with transit and walking, into transportation planning, design, and construction and to increase the bicycle's role in the transportation system. Eight influential elements that emerged from the review of policy documents and research literature were used to guide interviews: governance structure, location, access roads, self-perceived environmental stewardship, spending restrictions on nonaviation transportation improvements, proximity to transit, policies and mandates to reduce environmental impacts, and land use constraints. Seven cases were selected on the basis of their inclusion in studies on key aspects of airport ground access: Oakland International Airport, San Francisco International Airport, and Los Angeles International Airport, California; Seattle–Tacoma International Airport, Washington; Boston Logan International Airport, Massachusetts; Minneapolis–Saint Paul International Airport, Minnesota; and Portland International Airport, Oregon, an exemplar recommended by several informants. The discussion was limited to employee bicycle access, the focus of airport operators that invested in programs to reduce single-occupancy-vehicle travel at airports. During aggregation of the interviews, replicable approaches for improving bicycle access were identified, as were examples of innovative funding for multimodal access that used revenues generated through passenger facilities charges. The following areas are suggested for additional research: commute needs of airport employees, mode choice for ground access, and airport operator costs and benefits of bicycle access investments.

Dynamic server allocation at parallel queues

This article explores whether dynamically reassigning servers to parallel queues in response to queue imbalances can reduce average waiting time in those queues. Approximate dynamic programming methods are used to determine when servers should be switched, and the performance of such dynamic allocations is compared to that of a pre-scheduled deterministic allocation. The proposed method is tested on both synthetic data and data from airport security checkpoints at Boston Logan International Airport. It is found that in situations where the uncertainty in customer arrival rates is significant, dynamically reallocating servers can substantially reduce waiting time. Moreover, it is found that intuitive switching strategies that are optimal for queues with homogeneous entry rates are not optimal in this setting.

Pre‐travel Health Advice‐Seeking Behavior Among US International Travelers Departing From Boston Logan International Airport

Globally mobile populations are at higher risk of acquiring geographically restricted infections and may play a role in the international spread of infectious diseases. Despite this, data about sources of health information used by international travelers are limited. We surveyed 1,254 travelers embarking from Boston Logan International Airport regarding sources of health information. We focused our analysis on travelers to low or low-middle income (LLMI) countries, as defined by the World Bank 2009 World Development Report. A total of 476 survey respondents were traveling to LLMI countries. Compared with travelers to upper-middle or high income (UMHI) countries, travelers to LLMI countries were younger, more likely to be foreign-born, and more frequently reported visiting family as the purpose of their trip. Prior to their trips, 46% of these travelers did not pursue health information of any type. In a multivariate analysis, being foreign-born, traveling alone, traveling for less than 14 days, and traveling for vacation each predicted a higher odds of not pursuing health information among travelers to LLMI countries. The most commonly cited reason for not pursuing health information was a lack of concern about health problems related to the trip. Among travelers to LLMI countries who did pursue health information, the internet was the most common source, followed by primary care practitioners. Less than a third of travelers to LLMI countries who sought health information visited a travel medicine specialist. In our study, 46% of travelers to LLMI countries did not seek health advice prior to their trip, largely due to a lack of concern about health issues related to travel. Among travelers who sought medical advice, the internet and primary care providers were the most common sources of information. These results suggest the need for health outreach and education programs targeted at travelers and primary care practitioners.

Built Environment: New Default for Asphalt?

Built Environment: New Default for Asphalt?

Behavior Pattern Recognition and Aviation Security

Abstract New and evolving threats to aviation security require innovative and proactive solutions. Boston's Logan International Airport is adopting new methods to screen passengers based on techniques developed in Israel over the past 30 years. Behavior pattern recognition is a form of profiling passengers based not on race or religion but on suspicious and deceitful behavior. This technique is being used in addition to other aviation security measures in an attempt to form a more proactive approach to preventing future terrorist attacks at American airports.

I MPACT OF P ROMPT D EFIBRILLATION ON C ARDIAC A RREST AT A M AJOR I NTERNATIONAL A IRPORT

Objective. To describe the impact of a rapidly deployable, automated external defibrillator (AED)-equipped first-responder service at Boston's Logan International Airport on the rate of survival to hospital discharge after cardiac arrest. Methods. A prospective observational outcome study was undertaken for cardiac arrests taking place on the airport grounds from January 1, 1995, to December 31, 1999. Patients were included if they were unresponsive, they had no palpable pulse and no spontaneous respirations, an AED was turned on, and the cardac arrest took place on airport grounds. Airport fire rescue and emergency medical services (EMS) personnel submitted resuscitation records and AED memory modules for each cardiac arrest. Each author independently reviewed all cardiac arrest reports and code summaries to ensure accuracy and data integrity. Relevant dispatch and response times were determined from airport fire rescue and EMS dispatch records. Patient outcome was determined from hospital patient records. Descriptive statistics were calculated. Results. The airport fire rescue crew responded to 53 cardiac arrests. Of those, 38 met inclusion criteria. In 36 of 38 cases (94.7%), the airport fire rescue crew was first to apply the defibrillator, and the first to deliver a shock in 28 of 32 cases (87.5%) where a shock was delivered. The median response time for the airport fire rescue crew was 2 minutes, with a mode of 1 minute. The EMS response times were 5:29 (95% CI 4:37–6:19) for basic life support crews and 8:07 (95% CI 7:17–8:57) for advanced life support crews. All patients who survived to hospital admission (n = 15) and hospital discharge (n = 8) received their first shock by the airport fire rescue crew. Eight patients (21.1%) survived to hospital discharge. In five of the eight survivors to hospital discharge, defibrillation by the airport crew alone achieved a return of spontaneous circulation. Conclusions. A rapidly deployable first-responder service permits early defibrillation minutes before arrival of EMS personnel. This rapid response positively impacts the return of spontaneous circulation and survival to hospital discharge after cardiac arrest.

Queuing Model for Taxi-Out Time Estimation

Flights incur a large percentage of their delays on the ground during the departure process between their scheduled departure from the gate and takeoff. Because of the large uncertainties associated with them, these delays are difficult to predict and account for, hindering the ability to effectively manage the Air Traffic Control (ATC) system. This paper presents an effort to improve the accuracy of estimating the taxi-out time, which is the duration between pushback and takeoff. The method was to identify the main factors that affect the taxi-out time and build an estimation model that takes the most important ones into account. An analysis conducted at Boston Logan International Airport identified the runway configuration, the airline/terminal, the downstream restrictions and the takeoff queue size as the main causal factors that affect the taxi-out time. Of these factors the takeoff queue size was the most important one, where the queue size that an aircraft experienced was measured as the number of takeoffs that took place between its pushback time and its takeoff time. Consequently, a queuing model was built to estimate the taxi-out time at Logan Airport based on queue size estimation. For each aircraft, the queuing model assumes knowledge of the number of departure aircraft present on the airport surface at its pushback time and estimates its takeoff queue size by predicting the amount of passing that it may experience on the airport surface during its taxi out. The prediction performance of the queuing model was compared at Logan Airport with a running average model, which represents the baseline used currently in the Enhanced Traffic Management System (ETMS). The running average model uses a fourteen-day average as the estimate of the taxi-out time. The queuing model improved the mean absolute error in the taxi-out time estimation by approximately twenty percent and the accuracy rate by approximately ten percent, over the fourteen-day running average model.